Gene Editing of the Catfish Gonadotropin-Releasing Hormone Gene and Hormone Therapy to Control the Reproduction in Channel Catfish, Ictalurus punctatus
Bibliographic record
Abstract
Transcription activator-like effector nuclease (TALEN) plasmids targeting the channel catfish gonadotropin-releasing hormone (cfGnRH) gene were delivered into fertilized eggs with double electroporation to sterilize channel catfish (Ictalurus punctatus). Targeted cfGnRH fish were sequenced and base deletion, substitution, and insertion were detected. The gene mutagenesis was achieved in 52.9% of P1 fish. P1 mutants (individuals with human-induced sequence changes at the cfGnRH locus) had lower spawning rates (20.0−50.0%) when there was no hormone therapy compared to the control pairs (66.7%) as well as having lower average egg hatch rates (2.0% versus 32.3−74.3%) except for one cfGnRH mutated female that had a 66.0% hatch rate. After low fertility was observed in 2016, application of luteinizing hormone-releasing hormone analog (LHRHa) hormone therapy resulted in good spawning and hatch rates for mutants in 2017, which were not significantly different from the controls (p > 0.05). No exogenous DNA fragments were detected in the genome of mutant P1 fish, indicating no integration of the plasmids. No obvious effects on other economically important traits were observed after the knockout of the reproductive gene in the P1 fish. Growth rates, survival, and appearance between mutant and control individuals were not different. While complete knock-out of reproductive output was not achieved, as these were mosaic P1 brood stock, gene editing of channel catfish for the reproductive confinement of gene-engineered, domestic, and invasive fish to prevent gene flow into the natural environment appears promising.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".